A comparative study of correspondence-search algorithms in MIS images.

The ability to find image similarities (feature matching) between laparoscopic views is essential in many robotic-assisted Minimally-Invasive Surgery (MIS) applications. Differently from feature tracking methods, feature matching does not make any restrictive assumption about the sequential nature of the two images or about the organ motion, and could then be used, e.g., to recover tracked features that were lost due to a prolonged occlusion, a sudden endoscopic-camera retraction, or a strong illumination change. This paper provides researchers in the medical-imaging computing community with an extensive comparison of the most up-to-date feature-matching algorithms over a large (and annotated) data set of 100 MIS-image pairs obtained from real interventions. The accuracy of these methods, as well as their ability to consistently retrieve as many good matches as possible, are evaluated for popular feature detectors. In addition, the dataset and the software implementations of these methods are made freely available on the Internet.